Name
Abstract | ||
|---|---|---|
In this paper we propose a method for logo recognition based on Convolutional Neural Networks, instead of the commonly used keypoint-based approaches. The method involves the selection of candidate subwindows using an unsupervised segmentation algorithm, and the SVM-based classification of such candidate regions using features computed by a CNN. For training the neural network we augment the training set with artificial transformations, while for classification we exploit a query expansion strategy to increase the recall rate. Experiments were performed on a publicly-available dataset that was also corrupted in order to investigate the robustness of the proposed method with respect to blur, noise and lossy compression. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-23234-8_41 | IMAGE ANALYSIS AND PROCESSING - ICIAP 2015, PT II |
Field | DocType | Volume |
Computer vision,Lossy compression,Query expansion,Pattern recognition,Computer science,Convolutional neural network,Segmentation,Support vector machine,Robustness (computer science),Artificial intelligence,Deep learning,Artificial neural network | Conference | 9280 |
ISSN | Citations | PageRank |
0302-9743 | 10 | 0.67 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simone Bianco | 1 | 226 | 24.48 |
| Marco Buzzelli | 2 | 29 | 4.91 |
| Davide Mazzini | 3 | 28 | 2.48 |
| Raimondo Schettini | 4 | 1476 | 154.06 |